An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Guo, Jifa; Huo, Hongyuan

doi:10.3390/rs9090960

Cited by 11 publications

(6 citation statements)

References 45 publications

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“…Step 1 the computational complexity by Equations 29, 30, 31 and 9 is O(4nMC). In Step 2, the computational complexity of FCM algorithm is O(nMC), and the computational complexity by Equations 37,38,39,26,27,28,9,40,41,42,43, 50 and 48 is O(12nMC + MC). In Step 3, each iteration includes step 3.2, and Equation 50 causes a computational complexity of O(nMC)(MC + 8), while that of Step 3.3 is O(CM + 8).…”

Section: Computational Complexity: Inmentioning

confidence: 99%

“…In Step 3, each iteration includes step 3.2, and Equation 50 causes a computational complexity of O(nMC)(MC + 8), while that of Step 3.3 is O(CM + 8). For step 3.4 and step 3.5, the computational complexity due to Equations 37,38,39,26,27,28,9,40,41,42,and 43 is O(11nMC). Therefore, in each loop in step 3 there is a computational complexity of O(nM2C2 + 19nMC + CM + 8).…”

Section: Computational Complexity: Inmentioning

confidence: 99%

“…Recently, some studies have improved the IT2FCM algorithm for satellite image classification. Accordingly, in [38,40], a new distance was introduced to replace the traditional Euclidean distance in the IT2FCM algorithm using spectral and spatial information in multispectral remote sensing image clustering. In [39], the SIIT2FCM algorithm was expanded from IIT2FCM [38] to change detection on multispectral satellite images that used spatial information and the semi-supervised method to improve the accuracy of classification results.…”

Section: Introductionmentioning

confidence: 99%

“…Equations 37,38,40,41 and 35, we can use both MFs µ * ik , τ * ik and the centroid v * i , or use one of them depending on the additional information obtained. It is easy to see that, when µ * ik , τ * ik , v * i are not used and µ…”

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confidence: 99%

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A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis

Mai

Ngo

Hung

et al. 2021

Information Sciences

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Although satellite images can provide more information about the Earth's surface in a relatively short time and over a large scale, they are affected by observation conditions (e.g., wind, sun, rain, and humidity) and the accuracy of the image acquisition equipment. The objects on the images are often unclear and uncertain, especially at their borders. The fuzzy clustering technique allows each data pattern to belong to many different clusters through membership function (MF) values that can handle data patterns with unclear and uncertain boundaries well. However, this technique is quite sensitive to noise, outliers, and limitations in handling uncertainties. Furthermore, the membership degrees of type-1 fuzzy sets (T1FSs) are crisp, and in many cases, it is difficult to precisely determine the T1FS parameters. To overcome these disadvantages, we propose a hybrid method encompassing interval type-2 semi-supervised possibilistic fuzzy c-means clustering (IT2SPFCM) and particle swarm optimization (PSO) to form the proposed IT2SPFCMPSO. We experimented on several satellite images (Landsat-5 TM, Landsat-7 ETM+, Landsat-8, Sentinel-2A) to prove the effectiveness of the proposed method. Experimental results show that the IT2SPFCM-PSO algorithm achieves accuracies from 98.8% to 99.39%, which are higher than those of other matching algorithms. An analysis of the results by indicators PC-I, CE-I, D-I, XB-I, τ − I, and MSE also showed that the proposed method achieves better results in most experiments.

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Section: Computational Complexity: Inmentioning

confidence: 99%

Section: Computational Complexity: Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis

Mai

Ngo

Hung

et al. 2021

Information Sciences

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“…Ngo et al proposed a semi-supervised interval type-2 fuzzy c-means clustering (SIIT2FCM) with spatial information constraints, and it is applied to land cover segmentation from multispectral remote sensing images [12]. Guo et al constructed an enhanced IT2FCM* algorithm for remote sensing image segmentation by utilizing spatial information and spectral index [13]. Huo et al used spectral uncertainty and the ranking of interval numbers to construct an improved IT2FCM* algorithm to solve the problem of land cover segmentation [14].…”

mentioning

confidence: 99%

A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

Guo

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In the process of land cover segmentation from remote sensing image, there are some uncertainties such as "significant difference in class density", "different objects with same spectrum" and "same object with different spectra". Existing fuzzy c-means clustering is not sufficient to describe the high-order fuzzy uncertainties and cannot achieve accurate segmentation. Type-2 fuzzy set is perfect for handling with inter-class multiple uncertainties, and clustering algorithm can suppress the noise of remote sensing image effectively by incorporating local information. Therefore, on the basis of integrating local information, this paper proposes a robust single fuzzifier interval type-2 fuzzy local C-means clustering based on adaptive interval-valued data for land cover segmentation. Firstly, interval-valued data modeling is performed for remote sensing data, and remote sensing features are represented as interval-valued vectors, and the robust interval-valued distance measure that can maximize the distance between interval-valued numbers is used to generate an interval type-2 fuzzy set through robust fuzzy clustering. Secondly, this paper adopts an efficient type reduction method to seek equivalent type-1 fuzzy set adaptively, and realizes the segmentation of land cover by the principle of maximum type-1 fuzzy membership. The test results of multi-spectral remote sensing images show that the segmentation performance of this proposed algorithm outperforms existing state of the art adaptive interval type-2 fuzzy clustering algorithms, and it is beneficial to the interpretation of remote sensing image. Index Terms-fuzzy C-means clustering; interval-valued data; interval type-2 fuzzy sets; fuzzy local information; land cover segmentation. I. INTRODUCTIONuzzy theory, as the basis of fuzzy analysis, is constantly updated and developed. The defects of type-1 fuzzy clustering algorithm in dealing with uncertainty are gradually exposed, and type-2 fuzzy clustering algorithm has certain potential advantage in dealing with high order uncertainty. Zadeh introduced type-2 fuzzy set (T2FS) as the extension of the concept of ordinary fuzzy set. In essence, it is a "fuzzy fuzzy set". In particular, type 2 fuzzy set is widely used to deal

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A Novel Remote Sensing Image Classification Method Based on Semi-supervised Fuzzy C-Means

Feng

Fan

et al. 2019

Communications in Computer and Information Science

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An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Cited by 11 publications

References 45 publications

A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis

A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis

A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

A Novel Remote Sensing Image Classification Method Based on Semi-supervised Fuzzy C-Means

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